Filtering by
- Creators: Arizona State University
- Creators: Thrall, Charlotte
Aim: To reduce the fear of falling in an elderly population by teaching ‘Tai Chi for Falls Prevention’ classes twice a week for 12 weeks.
Background & Significance: Falls continue to be the leading cause of injury-related deaths of Arizonans who are 65 years or older - well above the national average. It is predicted that by 2030, national medical spending for this population will total over $31 billion, yearly. Tai Chi is revered for being a beneficial form of simple, low-impact exercise, which the CDC endorses for its falls risk reduction benefits.
Methods: The intervention consisted of 60-minute classes occurring twice a week for 12 weeks. Participants were English-speaking, between 65-85 years old, and able to ambulate independently. Appropriate pre-screening tools were used before applicants consented. Their Fear of Falling (FoF) was measured using a fall risk perception tool at the beginning, middle, and the end of the project. This ordinal data was analyzed with Friedman ANOVA using SPSS 25
Outcomes/Results: After enrolling five total participants, only three completed the project. This severely limited data analysis of their FoF, resulting in a statistical significance (p = 0.68), deeming the intervention ineffective - Despite observable downwards trending FoF scores.
Conclusion: The acceptance of the null hypothesis is attributed to the low enrollment and high attrition rate. Also, the only data measured was quantifiable, subjective data. Future projects could add objective data to reinforce the benefits of Tai Chi. This might reinforce the validity of Tai Chi as a practical recommendation due to its cost-effective simple interventional design and effectiveness for prevention of accidental falls. Increased focus on improved recruitment & retainment strategies should be prioritized for similar projects in the future.
Homeless individuals encounter barriers such as lack of health insurance, increased cost of care and unavailability of resources. They have increased risk of comorbid physical disease and poor mental health. Depression is a prevalent mental health disorder in the US linked to increased risk of mortality. Literature suggests depression screening can identify high-risk individuals with using the patient health questionnaire (PHQ-9).
The objective of this project is to determine if screening identifies depression in the homeless and how it impacts healthcare access. Setting is a local organization in Phoenix offering shelter to homeless individuals. An evidence-based project was implemented over two months in 2019 using convenience sampling. Intervention included depression screening using the PHQ-9, referring to primary care and tracking appointment times. IRB approval obtained from Arizona State University, privacy discussed, and consent obtained prior to data collection. Participants were assigned a random number to protect privacy.
A chart audit tool was used to obtain sociodemographics and insurance status. Descriptive statistics used and analyzed using Intellectus. Sample size was (n = 18), age (M = 35) most were White-non-Hispanic, 44% had a high school diploma and 78% were insured. Mean score was 7.72, three were previously diagnosed and not referred. Three were referred with a turnaround appointment time of one, two and seven days respectively. No significant correlation found between age and depression severity. A significant correlation found between previous diagnosis and depression severity. Attention to PHQ-9 varied among providers and not always addressed. Future projects should focus on improving collaboration between this facility and providers, increasing screening and ensuring adequate follow up and treatment.
As a step in model development, statistically designed screening experiments may be used to identify the main effects and interactions most significant on a response of a system. However, traditional approaches for screening are ineffective for complex systems because of the size of the experimental design. Consequently, the factors considered are often restricted, but this automatically restricts the interactions that may be identified as well. Alternatively, the designs are restricted to only identify main effects, but this then fails to consider any possible interactions of the factors.
To address this problem, a specific combinatorial design termed a locating array is proposed as a screening design for complex systems. Locating arrays exhibit logarithmic growth in the number of factors because their focus is on identification rather than on measurement. This makes practical the consideration of an order of magnitude more factors in experimentation than traditional screening designs.
As a proof-of-concept, a locating array is applied to screen for main effects and low-order interactions on the response of average transport control protocol (TCP) throughput in a simulation model of a mobile ad hoc network (MANET). A MANET is a collection of mobile wireless nodes that self-organize without the aid of any centralized control or fixed infrastructure. The full-factorial design for the MANET considered is infeasible (with over 10^{43} design points) yet a locating array has only 421 design points.
In conjunction with the locating array, a ``heavy hitters'' algorithm is developed to identify the influential main effects and two-way interactions, correcting for the non-normal distribution of the average throughput, and uneven coverage of terms in the locating array. The significance of the identified main effects and interactions is validated independently using the statistical software JMP.
The statistical characteristics used to evaluate traditional screening designs are also applied to locating arrays.
These include the matrix of covariance, fraction of design space, and aliasing, among others. The results lend additional support to the use of locating arrays as screening designs.
The use of locating arrays as screening designs for complex engineered systems is promising as they yield useful models. This facilitates quantitative evaluation of architectures and protocols and contributes to our understanding of complex engineered networks.